Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings

Abstract

SUMMARY:In this article, the underlying theory of clinical diffusion MR imaging, including diffusion tensor imaging (DTI) and fiber tractography, is reviewed. First, a brief explanation of the basic physics of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping is provided. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber orientation maps, and 3D fiber tractography. This article provides the requisite background for the second article in this 2-part review to appear next month, which covers the major technical factors that affect image quality in diffusion MR imaging, including the acquisition sequence, magnet field strength, gradient amplitude and slew rate, and multichannel radio-frequency coils and parallel imaging. The emphasis is on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature. Diffusion MR imaging of the brain was first adopted for use in clinical neuroradiology during the early 1990s and was found to have immediate utility for the evaluation of suspected acute ischemic stroke. Since that time, enormous strides forward in the technology of diffusion imaging have greatly improved image quality and enabled many new clinical applications. These include the diagnosis of intracranial pyogenic infections, masses, trauma, and vasogenic-versus-cytotoxic edema. Furthermore, the advent of diffusion tensor imaging (DTI) and fiber tractography has opened an entirely new noninvasive window on the white matter connectivity of the human brain. DTI and fiber tractography have already advanced the scientific understanding of many neurologic and psychiatric disorders and have been applied clinically for the presurgical mapping of eloquent white matter tracts before intracranial mass resections.This 2-part review explores the current state of the art for the acquisition and analysis of clinical diffusion imaging, including DTI and fiber tractography. This first article begins with an explanation of the basic physics and underlying theory of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber-orientation maps, and 3D fiber tractography. This article provides the necessary background for the second article, which focuses on the major technical factors that affect image quality in diffusion imaging, with an emphasis on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature. Theoretic Underpinnings of Diffusion ImagingBrownian Motion and Tissue Ultrastructure Diffusion, also known as "Brownian motion," refers to constant random microscopic molecular motion due to heat. At a fixed temperature, the rate of diffusion can be described by the Einstein equation 1 :where Ͻr 2 Ͼ refers...

Determination of Optimum Combination of Voxel Size and b-value for Brain Diffusion Tensor Imaging

“…This study has led to an understanding that the SNR improves with decreasing b-value and with increasing voxel size (Figure 1), which agrees well with the theory (Mukherjee et al 2008b) and as predicted via (1) and (2). Our findings also matched with previous work on SNR assessment (Laganà et al 2010;Lazar & Alexander 2003).…”

Heterogeneity in age-related white matter changes

“…However, the pool of bound protons can be indirectly quantified by utilizing magnetization transfer. MTI for mapping the magnetization transfer ratio is readily available on most clinical scanners and allows semi-quantitative assessment of tissue changes with a higher image resolution than provided by DTI [34,48,49]. Table 4 describes the results of DTI and MTI in various settings and disease entities.…”

Assessment of Angiographic Vascularity of Meningiomas with Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging and Diffusion Tensor Imaging

“…FA and ADC were calculated from diffusion-tensor data by use of standard algorithms described previously. 5,12,13 The CBV for each voxel was estimated by integrating the relaxivity-time curve converted from the dynamic signal intensity curve. Contrast leakage correction was performed by use of a technique outlined by Boxerman et al 14,15 …”

“…Fractional anisotropy (FA) and ADC are quantitative metrics derived from DTI for water diffusivity measurement. 5 DSC-PWI, on the other hand, measures T2*-weighted signal intensity loss that occurs dynamically over bolus injection of contrast medium, from which relative CBV, a quantitative marker of tumor angiogenesis, can be computed. 6 Both DTI 7,8 and DSC-PWI [9][10][11] had been reported to be useful in subtyping meningiomas.…”